PYME.localization.FitFactories.LatGaussFitFR module¶

PYME.localization.FitFactories.LatGaussFitFR.FitFactory¶: alias of GaussianFitFactory

PYME.localization.FitFactories.LatGaussFitFR.FitResult(fitResults, metadata, slicesUsed=None, resultCode=-1, fitErr=None, background=0, nchi2=-1)¶

class PYME.localization.FitFactories.LatGaussFitFR.GaussianFitFactory(data, metadata, fitfcn=<function f_gauss2d>, background=None, noiseSigma=None, **kwargs)¶

Bases: FFBase

Create a fit factory which will operate on image data (data), potentially using voxel sizes etc contained in metadata.

FromPoint(x, y, z=None, roiHalfSize=5, axialHalfSize=15)¶: This should be overridden in derived classes to actually do the fitting. The function which gets implemented should return a numpy record array, of the dtype defined in the module level FitResultsDType variable (the calling function uses FitResultsDType to pre-allocate an array for the results)

classmethod evalModel(params, md, x=0, y=0, roiHalfSize=5)¶

Evaluate the model that this factory fits - given metadata and fitted parameters.

Used for fit visualisation

PYME.localization.FitFactories.LatGaussFitFR.GaussianFitResultR(fitResults, metadata, slicesUsed=None, resultCode=-1, fitErr=None, background=0, nchi2=-1)¶

PYME.localization.FitFactories.LatGaussFitFR.f_J_gauss2d(p, X, Y)¶: generate the jacobian for a 2d Gaussian - for use with _fithelpers.weightedJacF

PYME.localization.FitFactories.LatGaussFitFR.f_gauss2d(p, X, Y)¶: 2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y]

PYME.localization.FitFactories.LatGaussFitFR.f_gauss2dF(p, X, Y)¶: 2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y] - uses fast exponential approx

PYME.localization.FitFactories.LatGaussFitFR.f_gauss2dSlow(p, X, Y)¶: 2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y]

PYME.localization.FitFactories.LatGaussFitFR.f_gauss2d_no_bg(p, X, Y)¶: 2D Gaussian model function with linear background - parameter vector [A, x0, y0, sigma, background, lin_x, lin_y]

PYME.localization.FitFactories.LatGaussFitFR.f_j_gauss2d(p, func, d, w, X, Y)¶: generate the jacobian for a 2d Gaussian

PYME.localization.FitFactories.LatGaussFitFR.genFitImage(fitResults, metadata)¶